knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
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## v tibble 3.0.3 v dplyr 1.0.0
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## v readr 1.3.1 v forcats 0.5.0
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## -- Conflicts ------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
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library(here)
## Warning: package 'here' was built under R version 4.0.2
## here() starts at C:/Users/atredennick/repos/COVID-stochastic-fitting
allfiles <- list.files("../output/current/", ".csv")
for(f in allfiles) {
fname <- paste0("../output/current/",f)
dat <- read.csv(fname) %>%
filter(variable %in% c("actual_daily_cases")) %>%
mutate(date = as.Date(date))
read.csv(fname) %>%
filter(variable %in% c("daily_cases")) %>%
filter(sim_type == "status_quo") %>%
mutate(date = as.Date(date)) %>%
filter(date <= (Sys.Date() + 7*4)) %>%
ggplot(aes(x = date, y = median_value)) +
geom_ribbon(aes(ymin = lower_80, ymax = upper_80), alpha = 0.2) +
geom_line() +
geom_line(data = dat, aes(x = date, y = mean_value), color = "blue") +
ylab("daily cases") +
ggtitle(f) -> out
print(out)
dat <- read.csv(fname) %>%
filter(variable %in% c("actual_daily_deaths")) %>%
mutate(date = as.Date(date))
read.csv(fname) %>%
filter(variable %in% c("daily_deaths")) %>%
filter(sim_type == "status_quo") %>%
mutate(date = as.Date(date)) %>%
filter(date <= (Sys.Date() + 7*4)) %>%
ggplot(aes(x = date, y = median_value)) +
geom_ribbon(aes(ymin = lower_80, ymax = upper_80), alpha = 0.2) +
geom_line() +
ylab("daily deaths") +
geom_line(data = dat, aes(x = date, y = mean_value), color = "blue") +
ggtitle(f) -> out3
print(out3)
read.csv(fname) %>%
filter(variable %in% c("combined_trend", "latent_trend", "mobility_trend")) %>%
filter(sim_type == "status_quo") %>%
mutate(date = as.Date(date)) %>%
ggplot(aes(x = date, y = mean_value)) +
geom_point(aes(color = variable)) +
ggtitle(f) -> out2
print(out2)
}
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